Overview

Dataset statistics

Number of variables34
Number of observations162928
Missing cells330958
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory174.3 MiB
Average record size in memory1.1 KiB

Variable types

CAT18
NUM16

Warnings

date has a high cardinality: 476 distinct values High cardinality
year has a high cardinality: 84 distinct values High cardinality
wdsp has a high cardinality: 319 distinct values High cardinality
mxpsd has a high cardinality: 114 distinct values High cardinality
count_slp is highly correlated with slpHigh correlation
slp is highly correlated with count_slpHigh correlation
count_stp is highly correlated with stpHigh correlation
stp is highly correlated with count_stpHigh correlation
count_visib is highly correlated with count_dewpHigh correlation
count_dewp is highly correlated with count_visibHigh correlation
flag_max is highly correlated with year and 1 other fieldsHigh correlation
year is highly correlated with flag_max and 1 other fieldsHigh correlation
flag_min is highly correlated with year and 1 other fieldsHigh correlation
date has 158200 (97.1%) missing values Missing
flag_max has 71753 (44.0%) missing values Missing
flag_min has 82202 (50.5%) missing values Missing
flag_prcp has 18803 (11.5%) missing values Missing
max is highly skewed (γ1 = 41.79360596) Skewed
min is highly skewed (γ1 = 30.95999655) Skewed
date is uniformly distributed Uniform
count_dewp has 8128 (5.0%) zeros Zeros
count_slp has 36605 (22.5%) zeros Zeros
count_stp has 53637 (32.9%) zeros Zeros
count_visib has 5791 (3.6%) zeros Zeros
prcp has 96446 (59.2%) zeros Zeros

Reproduction

Analysis started2021-05-10 07:14:49.115944
Analysis finished2021-05-10 07:15:52.312185
Duration1 minute and 3.2 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct44134
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20783.73501
Minimum0
Maximum44133
Zeros4
Zeros (%)< 0.1%
Memory size1.2 MiB
2021-05-10T00:15:52.394283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2036
Q110182.75
median20365.5
Q330548.25
95-th percentile41386
Maximum44133
Range44133
Interquartile range (IQR)20365.5

Descriptive statistics

Standard deviation12379.02819
Coefficient of variation (CV)0.5956113368
Kurtosis-1.103105746
Mean20783.73501
Median Absolute Deviation (MAD)10183
Skewness0.1211871724
Sum3386252377
Variance153240338.9
MonotocityNot monotonic
2021-05-10T00:15:52.520851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04< 0.1%
 
94174< 0.1%
 
298914< 0.1%
 
196524< 0.1%
 
176054< 0.1%
 
237504< 0.1%
 
217034< 0.1%
 
114644< 0.1%
 
155624< 0.1%
 
278404< 0.1%
 
135154< 0.1%
 
32764< 0.1%
 
12294< 0.1%
 
73744< 0.1%
 
53274< 0.1%
 
279684< 0.1%
 
257934< 0.1%
 
51994< 0.1%
 
9734< 0.1%
 
236224< 0.1%
 
50714< 0.1%
 
277124< 0.1%
 
256654< 0.1%
 
297634< 0.1%
 
195244< 0.1%
 
Other values (44109)16282899.9%
 
ValueCountFrequency (%) 
04< 0.1%
 
14< 0.1%
 
24< 0.1%
 
34< 0.1%
 
44< 0.1%
 
54< 0.1%
 
64< 0.1%
 
74< 0.1%
 
84< 0.1%
 
94< 0.1%
 
ValueCountFrequency (%) 
441331< 0.1%
 
441321< 0.1%
 
441311< 0.1%
 
441301< 0.1%
 
441291< 0.1%
 
441281< 0.1%
 
441271< 0.1%
 
441261< 0.1%
 
441251< 0.1%
 
441241< 0.1%
 

stn
Categorical

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
999999
28694 
726980
26449 
726985
19469 
726940
17640 
726930
17638 
Other values (5)
53038 
ValueCountFrequency (%) 
9999992869417.6%
 
7269802644916.2%
 
7269851946911.9%
 
7269401764010.8%
 
7269301763810.8%
 
726986144328.9%
 
726959113587.0%
 
726945113116.9%
 
72688187795.4%
 
72683671584.4%
 
2021-05-10T00:15:52.638927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:52.712589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:52.838812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length6
Mean length6
Min length6

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
930181930.9%
 
615582415.9%
 
713423413.7%
 
213423413.7%
 
8850668.7%
 
0617276.3%
 
5421384.3%
 
4289513.0%
 
3247962.5%
 
187790.9%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number977568100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
930181930.9%
 
615582415.9%
 
713423413.7%
 
213423413.7%
 
8850668.7%
 
0617276.3%
 
5421384.3%
 
4289513.0%
 
3247962.5%
 
187790.9%
 

Most occurring scripts

ValueCountFrequency (%) 
Common977568100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
930181930.9%
 
615582415.9%
 
713423413.7%
 
213423413.7%
 
8850668.7%
 
0617276.3%
 
5421384.3%
 
4289513.0%
 
3247962.5%
 
187790.9%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII977568100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
930181930.9%
 
615582415.9%
 
713423413.7%
 
213423413.7%
 
8850668.7%
 
0617276.3%
 
5421384.3%
 
4289513.0%
 
3247962.5%
 
187790.9%
 

wban
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
99999
39224 
24229
29371 
24232
26772 
24221
26770 
24242
5953 
Other values (7)
34838 
ValueCountFrequency (%) 
999993922424.1%
 
242292937118.0%
 
242322677216.4%
 
242212677016.4%
 
2424259533.7%
 
9426159493.7%
 
9428159483.7%
 
9427359443.6%
 
0420159393.6%
 
2420257303.5%
 
0423652423.2%
 
24248860.1%
 
2021-05-10T00:15:52.948459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:53.056573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
231298238.4%
 
924333229.9%
 
412974315.9%
 
1446065.5%
 
3379584.7%
 
0228502.8%
 
6111911.4%
 
860340.7%
 
759440.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number814640100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
231298238.4%
 
924333229.9%
 
412974315.9%
 
1446065.5%
 
3379584.7%
 
0228502.8%
 
6111911.4%
 
860340.7%
 
759440.7%
 

Most occurring scripts

ValueCountFrequency (%) 
Common814640100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
231298238.4%
 
924333229.9%
 
412974315.9%
 
1446065.5%
 
3379584.7%
 
0228502.8%
 
6111911.4%
 
860340.7%
 
759440.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII814640100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
231298238.4%
 
924333229.9%
 
412974315.9%
 
1446065.5%
 
3379584.7%
 
0228502.8%
 
6111911.4%
 
860340.7%
 
759440.7%
 

date
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct476
Distinct (%)10.1%
Missing158200
Missing (%)97.1%
Memory size1.2 MiB
2020-10-05
 
10
2021-01-04
 
10
2020-09-27
 
10
2020-04-26
 
10
2020-11-14
 
10
Other values (471)
4678 
ValueCountFrequency (%) 
2020-10-0510< 0.1%
 
2021-01-0410< 0.1%
 
2020-09-2710< 0.1%
 
2020-04-2610< 0.1%
 
2020-11-1410< 0.1%
 
2021-04-1510< 0.1%
 
2021-04-0710< 0.1%
 
2020-05-2110< 0.1%
 
2020-08-1410< 0.1%
 
2020-06-2910< 0.1%
 
2020-02-2310< 0.1%
 
2020-02-1410< 0.1%
 
2020-04-2210< 0.1%
 
2021-01-1510< 0.1%
 
2020-02-0410< 0.1%
 
2020-06-1410< 0.1%
 
2021-04-0810< 0.1%
 
2020-06-0110< 0.1%
 
2020-08-0910< 0.1%
 
2020-01-0610< 0.1%
 
2020-09-1710< 0.1%
 
2020-11-0910< 0.1%
 
2021-01-2410< 0.1%
 
2020-06-2310< 0.1%
 
2020-08-1310< 0.1%
 
Other values (451)44782.7%
 
(Missing)15820097.1%
 
2021-05-10T00:15:53.183251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:53.306403image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length3
Mean length3.203132672
Min length3

Overview of Unicode Properties

Unique unicode characters13
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n31640060.6%
 
a15820030.3%
 
0143882.8%
 
2122872.4%
 
-94561.8%
 
150511.0%
 
312850.2%
 
49620.2%
 
77760.1%
 
87760.1%
 
57750.1%
 
67660.1%
 
97580.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter47460090.9%
 
Decimal Number378247.2%
 
Dash Punctuation94561.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n31640066.7%
 
a15820033.3%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
01438838.0%
 
21228732.5%
 
1505113.4%
 
312853.4%
 
49622.5%
 
77762.1%
 
87762.1%
 
57752.0%
 
67662.0%
 
97582.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-9456100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin47460090.9%
 
Common472809.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n31640066.7%
 
a15820033.3%
 

Most frequent Common characters

ValueCountFrequency (%) 
01438830.4%
 
21228726.0%
 
-945620.0%
 
1505110.7%
 
312852.7%
 
49622.0%
 
77761.6%
 
87761.6%
 
57751.6%
 
67661.6%
 
97581.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII521880100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n31640060.6%
 
a15820030.3%
 
0143882.8%
 
2122872.4%
 
-94561.8%
 
150511.0%
 
312850.2%
 
49620.2%
 
77760.1%
 
87760.1%
 
57750.1%
 
67660.1%
 
97580.1%
 

year
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct84
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
2005
 
5442
2020
 
3655
2016
 
3655
2012
 
3652
2018
 
3650
Other values (79)
142874 
ValueCountFrequency (%) 
200554423.3%
 
202036552.2%
 
201636552.2%
 
201236522.2%
 
201836502.2%
 
201436502.2%
 
201536492.2%
 
201036462.2%
 
201936462.2%
 
200836442.2%
 
201336412.2%
 
200936192.2%
 
200736042.2%
 
201135752.2%
 
201735492.2%
 
200633802.1%
 
200432902.0%
 
200232782.0%
 
200332652.0%
 
200130501.9%
 
200029121.8%
 
199828991.8%
 
199928971.8%
 
199727691.7%
 
199323731.5%
 
Other values (59)7653847.0%
 
2021-05-10T00:15:53.435770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:53.548541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
113825521.2%
 
012990119.9%
 
912882819.8%
 
29636914.8%
 
8366805.6%
 
7296474.5%
 
5292934.5%
 
6261034.0%
 
4198943.1%
 
3167422.6%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number651712100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113825521.2%
 
012990119.9%
 
912882819.8%
 
29636914.8%
 
8366805.6%
 
7296474.5%
 
5292934.5%
 
6261034.0%
 
4198943.1%
 
3167422.6%
 

Most occurring scripts

ValueCountFrequency (%) 
Common651712100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
113825521.2%
 
012990119.9%
 
912882819.8%
 
29636914.8%
 
8366805.6%
 
7296474.5%
 
5292934.5%
 
6261034.0%
 
4198943.1%
 
3167422.6%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII651712100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
113825521.2%
 
012990119.9%
 
912882819.8%
 
29636914.8%
 
8366805.6%
 
7296474.5%
 
5292934.5%
 
6261034.0%
 
4198943.1%
 
3167422.6%
 

mo
Categorical

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
03
14081 
01
13981 
12
13782 
05
13756 
10
13747 
Other values (7)
93581 
ValueCountFrequency (%) 
03140818.6%
 
01139818.6%
 
12137828.5%
 
05137568.4%
 
10137478.4%
 
08137218.4%
 
07136938.4%
 
04135448.3%
 
11133198.2%
 
06132838.2%
 
09132668.1%
 
02127557.8%
 
2021-05-10T00:15:53.651958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:53.756268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
013582741.7%
 
16814820.9%
 
2265378.1%
 
3140814.3%
 
5137564.2%
 
8137214.2%
 
7136934.2%
 
4135444.2%
 
6132834.1%
 
9132664.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number325856100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
013582741.7%
 
16814820.9%
 
2265378.1%
 
3140814.3%
 
5137564.2%
 
8137214.2%
 
7136934.2%
 
4135444.2%
 
6132834.1%
 
9132664.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common325856100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
013582741.7%
 
16814820.9%
 
2265378.1%
 
3140814.3%
 
5137564.2%
 
8137214.2%
 
7136934.2%
 
4135444.2%
 
6132834.1%
 
9132664.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII325856100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
013582741.7%
 
16814820.9%
 
2265378.1%
 
3140814.3%
 
5137564.2%
 
8137214.2%
 
7136934.2%
 
4135444.2%
 
6132834.1%
 
9132664.1%
 

da
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
05
 
5370
18
 
5366
01
 
5364
04
 
5359
14
 
5359
Other values (26)
136110 
ValueCountFrequency (%) 
0553703.3%
 
1853663.3%
 
0153643.3%
 
0453593.3%
 
1453593.3%
 
1153593.3%
 
0253583.3%
 
0653583.3%
 
2253573.3%
 
1653573.3%
 
0853553.3%
 
0353553.3%
 
1553553.3%
 
1953533.3%
 
1053533.3%
 
1353523.3%
 
1253513.3%
 
2353513.3%
 
1753493.3%
 
2053493.3%
 
2553483.3%
 
2453483.3%
 
0753473.3%
 
2853463.3%
 
2753463.3%
 
Other values (6)2906317.8%
 
2021-05-10T00:15:53.863498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:53.964883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
17274522.3%
 
26920321.2%
 
06381119.6%
 
3240847.4%
 
5160734.9%
 
8160674.9%
 
4160664.9%
 
6160564.9%
 
7160424.9%
 
9157094.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number325856100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
17274522.3%
 
26920321.2%
 
06381119.6%
 
3240847.4%
 
5160734.9%
 
8160674.9%
 
4160664.9%
 
6160564.9%
 
7160424.9%
 
9157094.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common325856100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
17274522.3%
 
26920321.2%
 
06381119.6%
 
3240847.4%
 
5160734.9%
 
8160674.9%
 
4160664.9%
 
6160564.9%
 
7160424.9%
 
9157094.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII325856100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
17274522.3%
 
26920321.2%
 
06381119.6%
 
3240847.4%
 
5160734.9%
 
8160674.9%
 
4160664.9%
 
6160564.9%
 
7160424.9%
 
9157094.8%
 

temp
Real number (ℝ≥0)

Distinct808
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.07453108
Minimum0.1
Maximum97.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:54.074919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile35.3
Q144.5
median52.5
Q362
95-th percentile71.6
Maximum97.9
Range97.8
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation11.46650351
Coefficient of variation (CV)0.2160453098
Kurtosis-0.4315387374
Mean53.07453108
Median Absolute Deviation (MAD)8.7
Skewness0.02901709394
Sum8647327.2
Variance131.4807027
MonotocityNot monotonic
2021-05-10T00:15:54.203873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
455950.4%
 
475920.4%
 
47.55910.4%
 
47.85860.4%
 
46.25830.4%
 
485790.4%
 
45.35780.4%
 
46.85730.4%
 
45.45720.4%
 
45.95700.3%
 
495700.3%
 
45.15660.3%
 
49.95650.3%
 
43.65630.3%
 
48.75610.3%
 
46.65590.3%
 
46.55590.3%
 
44.85580.3%
 
47.35540.3%
 
47.25520.3%
 
48.15500.3%
 
43.25480.3%
 
45.65430.3%
 
47.15420.3%
 
48.45410.3%
 
Other values (783)14877891.3%
 
ValueCountFrequency (%) 
0.11< 0.1%
 
4.11< 0.1%
 
4.22< 0.1%
 
4.41< 0.1%
 
5.21< 0.1%
 
5.91< 0.1%
 
6.11< 0.1%
 
6.41< 0.1%
 
71< 0.1%
 
7.72< 0.1%
 
ValueCountFrequency (%) 
97.91< 0.1%
 
95.41< 0.1%
 
94.21< 0.1%
 
92.41< 0.1%
 
92.21< 0.1%
 
91.81< 0.1%
 
91.71< 0.1%
 
91.61< 0.1%
 
91.11< 0.1%
 
912< 0.1%
 

count_temp
Real number (ℝ≥0)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.83571271
Minimum4
Maximum24
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:54.319121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8
Q124
median24
Q324
95-th percentile24
Maximum24
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.951519134
Coefficient of variation (CV)0.2267624236
Kurtosis3.605691835
Mean21.83571271
Median Absolute Deviation (MAD)0
Skewness-2.236184102
Sum3557649
Variance24.51754173
MonotocityNot monotonic
2021-05-10T00:15:54.423605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
2412583577.2%
 
895285.8%
 
1666044.1%
 
2363273.9%
 
2224501.5%
 
1521681.3%
 
1413300.8%
 
2111330.7%
 
58880.5%
 
78630.5%
 
48480.5%
 
67840.5%
 
137320.4%
 
206580.4%
 
175690.3%
 
95490.3%
 
195490.3%
 
183760.2%
 
123590.2%
 
112130.1%
 
101650.1%
 
ValueCountFrequency (%) 
48480.5%
 
58880.5%
 
67840.5%
 
78630.5%
 
895285.8%
 
95490.3%
 
101650.1%
 
112130.1%
 
123590.2%
 
137320.4%
 
ValueCountFrequency (%) 
2412583577.2%
 
2363273.9%
 
2224501.5%
 
2111330.7%
 
206580.4%
 
195490.3%
 
183760.2%
 
175690.3%
 
1666044.1%
 
1521681.3%
 

dewp
Real number (ℝ)

Distinct714
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540.5378302
Minimum-10.7
Maximum9999.9
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2021-05-10T00:15:54.547574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-10.7
5-th percentile29.5
Q138.6
median45.2
Q351.4
95-th percentile66.5
Maximum9999.9
Range10010.6
Interquartile range (IQR)12.8

Descriptive statistics

Standard deviation2167.568809
Coefficient of variation (CV)4.010022403
Kurtosis15.09773082
Mean540.5378302
Median Absolute Deviation (MAD)6.4
Skewness4.134878578
Sum88068747.6
Variance4698354.54
MonotocityNot monotonic
2021-05-10T00:15:54.670054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9999.981285.0%
 
46.97930.5%
 
48.47580.5%
 
467490.5%
 
47.57460.5%
 
457320.4%
 
497280.4%
 
46.27170.4%
 
47.87080.4%
 
45.77070.4%
 
487050.4%
 
42.67040.4%
 
437030.4%
 
46.67010.4%
 
49.37010.4%
 
48.67010.4%
 
50.87000.4%
 
47.16960.4%
 
46.56950.4%
 
49.56910.4%
 
45.16910.4%
 
48.76900.4%
 
47.26860.4%
 
50.56850.4%
 
43.26840.4%
 
Other values (689)13772984.5%
 
ValueCountFrequency (%) 
-10.71< 0.1%
 
-102< 0.1%
 
-9.81< 0.1%
 
-8.61< 0.1%
 
-7.51< 0.1%
 
-7.31< 0.1%
 
-71< 0.1%
 
-6.71< 0.1%
 
-6.61< 0.1%
 
-5.61< 0.1%
 
ValueCountFrequency (%) 
9999.981285.0%
 
71.61< 0.1%
 
69.91< 0.1%
 
69.61< 0.1%
 
68.81< 0.1%
 
68.11< 0.1%
 
67.82< 0.1%
 
67.52< 0.1%
 
672< 0.1%
 
66.91< 0.1%
 

count_dewp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.92032063
Minimum0
Maximum24
Zeros8128
Zeros (%)5.0%
Memory size1.2 MiB
2021-05-10T00:15:54.777993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q123
median24
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.569139514
Coefficient of variation (CV)0.3140075924
Kurtosis3.301621328
Mean20.92032063
Median Absolute Deviation (MAD)0
Skewness-2.127208477
Sum3408506
Variance43.15359396
MonotocityNot monotonic
2021-05-10T00:15:54.884623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
2412002273.7%
 
893715.8%
 
081285.0%
 
1664914.0%
 
2364323.9%
 
2225031.5%
 
1521641.3%
 
1413350.8%
 
2111250.7%
 
137840.5%
 
196250.4%
 
206090.4%
 
175780.4%
 
75200.3%
 
94810.3%
 
184160.3%
 
123840.2%
 
62550.2%
 
112360.1%
 
101830.1%
 
41470.1%
 
51390.1%
 
ValueCountFrequency (%) 
081285.0%
 
41470.1%
 
51390.1%
 
62550.2%
 
75200.3%
 
893715.8%
 
94810.3%
 
101830.1%
 
112360.1%
 
123840.2%
 
ValueCountFrequency (%) 
2412002273.7%
 
2364323.9%
 
2225031.5%
 
2111250.7%
 
206090.4%
 
196250.4%
 
184160.3%
 
175780.4%
 
1664914.0%
 
1521641.3%
 

slp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct551
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3035.82154
Minimum981
Maximum9999.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:55.000529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum981
5-th percentile1008.4
Q11015.3
median1019.8
Q31029.8
95-th percentile9999.9
Maximum9999.9
Range9018.9
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation3748.818994
Coefficient of variation (CV)1.234861452
Kurtosis-0.2592299474
Mean3035.82154
Median Absolute Deviation (MAD)5.7
Skewness1.319378712
Sum494620331.9
Variance14053643.85
MonotocityNot monotonic
2021-05-10T00:15:55.120959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9999.93660522.5%
 
1017.39830.6%
 
1016.99820.6%
 
1016.59780.6%
 
1016.79730.6%
 
1017.69640.6%
 
1016.69640.6%
 
1017.19610.6%
 
1018.69530.6%
 
1018.49530.6%
 
1017.99490.6%
 
1018.29480.6%
 
1017.89460.6%
 
1018.89460.6%
 
1017.29430.6%
 
1018.19430.6%
 
1017.49410.6%
 
1016.39370.6%
 
1016.49350.6%
 
1017.59230.6%
 
1016.89230.6%
 
1017.79210.6%
 
1018.59090.6%
 
1016.29040.6%
 
10179030.6%
 
Other values (526)10364163.6%
 
ValueCountFrequency (%) 
9811< 0.1%
 
981.41< 0.1%
 
981.91< 0.1%
 
9824< 0.1%
 
982.11< 0.1%
 
982.61< 0.1%
 
983.41< 0.1%
 
983.62< 0.1%
 
9841< 0.1%
 
984.12< 0.1%
 
ValueCountFrequency (%) 
9999.93660522.5%
 
1040.12< 0.1%
 
10402< 0.1%
 
1039.82< 0.1%
 
1039.71< 0.1%
 
1039.41< 0.1%
 
1039.34< 0.1%
 
1039.21< 0.1%
 
1039.12< 0.1%
 
10395< 0.1%
 

count_slp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.52616493
Minimum0
Maximum24
Zeros36605
Zeros (%)22.5%
Memory size1.2 MiB
2021-05-10T00:15:55.226853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median23
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.770048945
Coefficient of variation (CV)0.5911867021
Kurtosis-0.9579562304
Mean16.52616493
Median Absolute Deviation (MAD)1
Skewness-0.8975721246
Sum2692575
Variance95.45385638
MonotocityNot monotonic
2021-05-10T00:15:55.332294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
247172144.0%
 
03660522.5%
 
23103696.4%
 
886995.3%
 
2273184.5%
 
2153543.3%
 
1652843.2%
 
2044002.7%
 
1936062.2%
 
1829441.8%
 
1723151.4%
 
1513390.8%
 
149800.6%
 
137150.4%
 
124500.3%
 
113150.2%
 
102260.1%
 
91370.1%
 
762< 0.1%
 
642< 0.1%
 
525< 0.1%
 
422< 0.1%
 
ValueCountFrequency (%) 
03660522.5%
 
422< 0.1%
 
525< 0.1%
 
642< 0.1%
 
762< 0.1%
 
886995.3%
 
91370.1%
 
102260.1%
 
113150.2%
 
124500.3%
 
ValueCountFrequency (%) 
247172144.0%
 
23103696.4%
 
2273184.5%
 
2153543.3%
 
2044002.7%
 
1936062.2%
 
1829441.8%
 
1723151.4%
 
1652843.2%
 
1513390.8%
 

stp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct942
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3878.491119
Minimum0
Maximum9999.9
Zeros6
Zeros (%)< 0.1%
Memory size1.2 MiB
2021-05-10T00:15:55.448202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile990.7
Q11007.3
median1015.2
Q39999.9
95-th percentile9999.9
Maximum9999.9
Range9999.9
Interquartile range (IQR)8992.6

Descriptive statistics

Standard deviation4242.002663
Coefficient of variation (CV)1.093724991
Kurtosis-1.4329287
Mean3878.491119
Median Absolute Deviation (MAD)11.4
Skewness0.7447477785
Sum631914801.1
Variance17994586.59
MonotocityNot monotonic
2021-05-10T00:15:55.571842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9999.95277632.4%
 
999.910420.6%
 
1010.85680.3%
 
1010.75670.3%
 
10115660.3%
 
1011.95650.3%
 
10105610.3%
 
1010.35580.3%
 
1009.15570.3%
 
1010.65560.3%
 
1008.85560.3%
 
1011.15530.3%
 
1011.65520.3%
 
1011.85510.3%
 
1009.65500.3%
 
1012.25490.3%
 
1011.35480.3%
 
10125480.3%
 
10135450.3%
 
1012.45450.3%
 
1013.15420.3%
 
1009.85400.3%
 
1012.15390.3%
 
1011.75370.3%
 
1011.55370.3%
 
Other values (917)9642059.2%
 
ValueCountFrequency (%) 
06< 0.1%
 
0.18< 0.1%
 
0.29< 0.1%
 
0.310< 0.1%
 
0.415< 0.1%
 
0.59< 0.1%
 
0.613< 0.1%
 
0.713< 0.1%
 
0.810< 0.1%
 
0.910< 0.1%
 
ValueCountFrequency (%) 
9999.95277632.4%
 
1038.91< 0.1%
 
1038.72< 0.1%
 
1038.31< 0.1%
 
1037.92< 0.1%
 
1037.81< 0.1%
 
1037.61< 0.1%
 
1037.51< 0.1%
 
1037.41< 0.1%
 
1037.22< 0.1%
 

count_stp
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.88292988
Minimum0
Maximum24
Zeros53637
Zeros (%)32.9%
Memory size1.2 MiB
2021-05-10T00:15:55.676372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)24

Descriptive statistics

Standard deviation11.12975717
Coefficient of variation (CV)0.7478203053
Kurtosis-1.679062675
Mean14.88292988
Median Absolute Deviation (MAD)0
Skewness-0.4889423582
Sum2424846
Variance123.8714946
MonotocityNot monotonic
2021-05-10T00:15:55.775992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
249058955.6%
 
05363732.9%
 
886465.3%
 
1636802.3%
 
2328461.7%
 
2210060.6%
 
46240.4%
 
214790.3%
 
203150.2%
 
192140.1%
 
181690.1%
 
171610.1%
 
151150.1%
 
1472< 0.1%
 
1364< 0.1%
 
1261< 0.1%
 
1152< 0.1%
 
1048< 0.1%
 
748< 0.1%
 
641< 0.1%
 
934< 0.1%
 
527< 0.1%
 
ValueCountFrequency (%) 
05363732.9%
 
46240.4%
 
527< 0.1%
 
641< 0.1%
 
748< 0.1%
 
886465.3%
 
934< 0.1%
 
1048< 0.1%
 
1152< 0.1%
 
1261< 0.1%
 
ValueCountFrequency (%) 
249058955.6%
 
2328461.7%
 
2210060.6%
 
214790.3%
 
203150.2%
 
192140.1%
 
181690.1%
 
171610.1%
 
1636802.3%
 
151150.1%
 

visib
Real number (ℝ≥0)

Distinct750
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.37121612
Minimum0
Maximum999.9
Zeros1
Zeros (%)< 0.1%
Memory size1.2 MiB
2021-05-10T00:15:55.886032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.3
Q19.4
median10
Q316.7
95-th percentile39.4
Maximum999.9
Range999.9
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation182.8855852
Coefficient of variation (CV)3.780876313
Kurtosis23.04791059
Mean48.37121612
Median Absolute Deviation (MAD)2
Skewness4.998312361
Sum7881025.5
Variance33447.13729
MonotocityNot monotonic
2021-05-10T00:15:56.008849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
103183919.5%
 
9.983475.1%
 
999.957913.6%
 
9.846782.9%
 
9.737822.3%
 
9.530251.9%
 
9.629401.8%
 
9.323641.5%
 
9.423311.4%
 
9.218831.2%
 
917511.1%
 
9.116321.0%
 
8.914510.9%
 
8.813320.8%
 
8.712370.8%
 
8.512100.7%
 
8.611290.7%
 
8.39910.6%
 
8.49400.6%
 
89150.6%
 
8.28990.6%
 
8.18760.5%
 
7.87700.5%
 
7.97430.5%
 
7.67060.4%
 
Other values (725)7936648.7%
 
ValueCountFrequency (%) 
01< 0.1%
 
0.120< 0.1%
 
0.228< 0.1%
 
0.351< 0.1%
 
0.452< 0.1%
 
0.563< 0.1%
 
0.677< 0.1%
 
0.767< 0.1%
 
0.81000.1%
 
0.9840.1%
 
ValueCountFrequency (%) 
999.957913.6%
 
99.43080.2%
 
95.74< 0.1%
 
95.635< 0.1%
 
95.513< 0.1%
 
95.45< 0.1%
 
95.33< 0.1%
 
94.51< 0.1%
 
94.41< 0.1%
 
94.11< 0.1%
 

count_visib
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.95806123
Minimum0
Maximum24
Zeros5791
Zeros (%)3.6%
Memory size1.2 MiB
2021-05-10T00:15:56.112501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q123
median24
Q324
95-th percentile24
Maximum24
Range24
Interquartile range (IQR)1

Descriptive statistics

Standard deviation6.375430985
Coefficient of variation (CV)0.3041994636
Kurtosis3.04108391
Mean20.95806123
Median Absolute Deviation (MAD)0
Skewness-2.064100215
Sum3414655
Variance40.64612024
MonotocityNot monotonic
2021-05-10T00:15:56.216972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
2411926673.2%
 
895265.8%
 
2368284.2%
 
1665824.0%
 
057913.6%
 
2225211.5%
 
1522611.4%
 
1414110.9%
 
2110620.7%
 
58800.5%
 
48560.5%
 
138210.5%
 
67710.5%
 
77570.5%
 
176180.4%
 
95790.4%
 
195510.3%
 
205370.3%
 
124480.3%
 
183300.2%
 
112900.2%
 
102420.1%
 
ValueCountFrequency (%) 
057913.6%
 
48560.5%
 
58800.5%
 
67710.5%
 
77570.5%
 
895265.8%
 
95790.4%
 
102420.1%
 
112900.2%
 
124480.3%
 
ValueCountFrequency (%) 
2411926673.2%
 
2368284.2%
 
2225211.5%
 
2110620.7%
 
205370.3%
 
195510.3%
 
183300.2%
 
176180.4%
 
1665824.0%
 
1522611.4%
 

wdsp
Categorical

HIGH CARDINALITY

Distinct319
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
999.9
 
5327
4.5
 
2477
4.8
 
2450
5.1
 
2445
4.2
 
2419
Other values (314)
147810 
ValueCountFrequency (%) 
999.953273.3%
 
4.524771.5%
 
4.824501.5%
 
5.124451.5%
 
4.224191.5%
 
4.623571.4%
 
5.323271.4%
 
4.322871.4%
 
4.922831.4%
 
3.922591.4%
 
4.022451.4%
 
3.422391.4%
 
3.722141.4%
 
4.122111.4%
 
4.422031.4%
 
5.021881.3%
 
3.621851.3%
 
3.521851.3%
 
4.721801.3%
 
5.221681.3%
 
5.921671.3%
 
5.421561.3%
 
3.821341.3%
 
5.621111.3%
 
5.521041.3%
 
Other values (294)10360763.6%
 
2021-05-10T00:15:56.343799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique33 ?
Unique (%)< 0.1%
2021-05-10T00:15:56.466350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length3
Mean length3.155381518
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.16249131.6%
 
9439478.5%
 
1425798.3%
 
4400767.8%
 
5388147.5%
 
3385687.5%
 
2349986.8%
 
6337376.6%
 
7293045.7%
 
8256765.0%
 
0239104.7%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number35160968.4%
 
Other Punctuation16249131.6%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
94394712.5%
 
14257912.1%
 
44007611.4%
 
53881411.0%
 
33856811.0%
 
23499810.0%
 
6337379.6%
 
7293048.3%
 
8256767.3%
 
0239106.8%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.162491100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common514100100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.16249131.6%
 
9439478.5%
 
1425798.3%
 
4400767.8%
 
5388147.5%
 
3385687.5%
 
2349986.8%
 
6337376.6%
 
7293045.7%
 
8256765.0%
 
0239104.7%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII514100100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.16249131.6%
 
9439478.5%
 
1425798.3%
 
4400767.8%
 
5388147.5%
 
3385687.5%
 
2349986.8%
 
6337376.6%
 
7293045.7%
 
8256765.0%
 
0239104.7%
 

count_wdsp
Categorical

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
24
116891 
8
 
9561
23
 
7851
16
 
6666
0
 
5327
Other values (17)
16632 
ValueCountFrequency (%) 
2411689171.7%
 
895615.9%
 
2378514.8%
 
1666664.1%
 
053273.3%
 
2229021.8%
 
1522741.4%
 
1414680.9%
 
2113470.8%
 
78960.5%
 
58920.5%
 
48640.5%
 
138240.5%
 
198170.5%
 
68060.5%
 
208000.5%
 
176720.4%
 
95740.4%
 
185400.3%
 
124530.3%
 
112830.2%
 
102200.1%
 
2021-05-10T00:15:56.593506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:56.707969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length1.883875086
Min length1

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
213314643.4%
 
411922338.8%
 
1158475.2%
 
8101013.3%
 
386752.8%
 
674722.4%
 
063472.1%
 
531661.0%
 
715680.5%
 
913910.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number306936100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
213314643.4%
 
411922338.8%
 
1158475.2%
 
8101013.3%
 
386752.8%
 
674722.4%
 
063472.1%
 
531661.0%
 
715680.5%
 
913910.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common306936100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
213314643.4%
 
411922338.8%
 
1158475.2%
 
8101013.3%
 
386752.8%
 
674722.4%
 
063472.1%
 
531661.0%
 
715680.5%
 
913910.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII306936100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
213314643.4%
 
411922338.8%
 
1158475.2%
 
8101013.3%
 
386752.8%
 
674722.4%
 
063472.1%
 
531661.0%
 
715680.5%
 
913910.5%
 

mxpsd
Categorical

HIGH CARDINALITY

Distinct114
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
12.0
12659 
8.0
12160 
11.1
12083 
8.9
11875 
13.0
10752 
Other values (109)
103399 
ValueCountFrequency (%) 
12.0126597.8%
 
8.0121607.5%
 
11.1120837.4%
 
8.9118757.3%
 
13.0107526.6%
 
14.0101796.2%
 
9.9100126.1%
 
7.095765.9%
 
15.093205.7%
 
15.970504.3%
 
6.068024.2%
 
10.165164.0%
 
999.956303.5%
 
5.148593.0%
 
17.143132.6%
 
18.141772.6%
 
20.027271.7%
 
4.123871.5%
 
19.023041.4%
 
22.016011.0%
 
21.013800.8%
 
11.813240.8%
 
16.911830.7%
 
22.910860.7%
 
10.99480.6%
 
Other values (89)100256.2%
 
2021-05-10T00:15:56.834323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique13 ?
Unique (%)< 0.1%
2021-05-10T00:15:57.286877image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length4
Mean length3.634329274
Min length1

Overview of Unicode Properties

Unique unicode characters11
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
.16057227.1%
 
113688823.1%
 
09109115.4%
 
97037111.9%
 
8327585.5%
 
2276494.7%
 
5228053.9%
 
7150472.5%
 
4140842.4%
 
3120832.0%
 
687861.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number43156272.9%
 
Other Punctuation16057227.1%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
113688831.7%
 
09109121.1%
 
97037116.3%
 
8327587.6%
 
2276496.4%
 
5228055.3%
 
7150473.5%
 
4140843.3%
 
3120832.8%
 
687862.0%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
.160572100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common592134100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
.16057227.1%
 
113688823.1%
 
09109115.4%
 
97037111.9%
 
8327585.5%
 
2276494.7%
 
5228053.9%
 
7150472.5%
 
4140842.4%
 
3120832.0%
 
687861.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII592134100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
.16057227.1%
 
113688823.1%
 
09109115.4%
 
97037111.9%
 
8327585.5%
 
2276494.7%
 
5228053.9%
 
7150472.5%
 
4140842.4%
 
3120832.0%
 
687861.5%
 

gust
Real number (ℝ≥0)

Distinct102
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean760.1792596
Minimum9.9
Maximum999.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:57.411412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9.9
5-th percentile17.1
Q1999.9
median999.9
Q3999.9
95-th percentile999.9
Maximum999.9
Range990
Interquartile range (IQR)0

Descriptive statistics

Standard deviation420.6937844
Coefficient of variation (CV)0.5534139207
Kurtosis-0.5950043983
Mean760.1792596
Median Absolute Deviation (MAD)0
Skewness-1.185222605
Sum123854486.4
Variance176983.2603
MonotocityNot monotonic
2021-05-10T00:15:57.539979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
999.912299175.5%
 
2030831.9%
 
18.129341.8%
 
1928551.8%
 
2127371.7%
 
15.927361.7%
 
2226691.6%
 
1522761.4%
 
22.921061.3%
 
17.119651.2%
 
25.115090.9%
 
1413760.8%
 
2811700.7%
 
2610940.7%
 
24.110850.7%
 
16.910630.7%
 
29.99810.6%
 
279450.6%
 
28.97970.5%
 
23.97640.5%
 
19.86090.4%
 
32.14430.3%
 
24.94260.3%
 
354060.2%
 
343650.2%
 
Other values (77)35432.2%
 
ValueCountFrequency (%) 
9.91< 0.1%
 
11.13< 0.1%
 
11.82< 0.1%
 
127< 0.1%
 
12.86< 0.1%
 
136< 0.1%
 
13.85< 0.1%
 
1413760.8%
 
1522761.4%
 
15.927361.7%
 
ValueCountFrequency (%) 
999.912299175.5%
 
871< 0.1%
 
821< 0.1%
 
71.11< 0.1%
 
59.81< 0.1%
 
59.11< 0.1%
 
57.91< 0.1%
 
56.92< 0.1%
 
551< 0.1%
 
542< 0.1%
 

max
Real number (ℝ≥0)

SKEWED

Distinct465
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.16207957
Minimum10
Maximum9999.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:57.675816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile43
Q153.1
median63
Q375.9
95-th percentile90
Maximum9999.9
Range9989.9
Interquartile range (IQR)22.8

Descriptive statistics

Standard deviation236.5013878
Coefficient of variation (CV)3.37078646
Kurtosis1751.797988
Mean70.16207957
Median Absolute Deviation (MAD)11
Skewness41.79360596
Sum11431367.3
Variance55932.90645
MonotocityNot monotonic
2021-05-10T00:15:57.810210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5042162.6%
 
5938112.3%
 
55.937982.3%
 
5237762.3%
 
5437722.3%
 
53.136032.2%
 
57.935622.2%
 
5535092.2%
 
5733712.1%
 
51.132822.0%
 
60.131731.9%
 
6130951.9%
 
48.930691.9%
 
6329061.8%
 
62.128831.8%
 
6828731.8%
 
64.927371.7%
 
66.927101.7%
 
7026901.7%
 
46.926821.6%
 
7226561.6%
 
6426041.6%
 
4825791.6%
 
7725591.6%
 
73.924821.5%
 
Other values (440)8453051.9%
 
ValueCountFrequency (%) 
101< 0.1%
 
10.21< 0.1%
 
12.41< 0.1%
 
12.91< 0.1%
 
141< 0.1%
 
14.22< 0.1%
 
15.11< 0.1%
 
15.32< 0.1%
 
163< 0.1%
 
16.21< 0.1%
 
ValueCountFrequency (%) 
9999.9920.1%
 
1089< 0.1%
 
107.64< 0.1%
 
107.22< 0.1%
 
107.18< 0.1%
 
106.92< 0.1%
 
10628< 0.1%
 
105.88< 0.1%
 
105.135< 0.1%
 
104.92< 0.1%
 

flag_max
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing71753
Missing (%)44.0%
Memory size1.2 MiB
*
81080 
10095 
ValueCountFrequency (%) 
*8108049.8%
 
100956.2%
 
(Missing)7175344.0%
 
2021-05-10T00:15:57.938560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:58.005927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:58.081094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.818834086
Min length0

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n14350648.4%
 
*8108027.4%
 
a7175324.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter21525972.6%
 
Other Punctuation8108027.4%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
*81080100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n14350666.7%
 
a7175333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin21525972.6%
 
Common8108027.4%
 

Most frequent Common characters

ValueCountFrequency (%) 
*81080100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n14350666.7%
 
a7175333.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII296339100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n14350648.4%
 
*8108027.4%
 
a7175324.2%
 

min
Real number (ℝ)

SKEWED

Distinct369
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.8338935
Minimum-11
Maximum9999.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:58.196872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile28
Q137
median44.1
Q351.1
95-th percentile57.9
Maximum9999.9
Range10010.9
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation320.6393241
Coefficient of variation (CV)5.95608646
Kurtosis957.3904759
Mean53.8338935
Median Absolute Deviation (MAD)7.1
Skewness30.95999655
Sum8771048.6
Variance102809.5762
MonotocityNot monotonic
2021-05-10T00:15:58.324592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5060303.7%
 
4153183.3%
 
42.149943.1%
 
4548743.0%
 
44.148133.0%
 
46.948103.0%
 
4346692.9%
 
39.946192.8%
 
4645602.8%
 
4845432.8%
 
48.945202.8%
 
3944662.7%
 
37.943092.6%
 
3742562.6%
 
51.141392.5%
 
5239862.4%
 
35.139302.4%
 
3639012.4%
 
53.138842.4%
 
3236732.3%
 
5436302.2%
 
3435882.2%
 
33.133292.0%
 
5531962.0%
 
55.930121.8%
 
Other values (344)5587934.3%
 
ValueCountFrequency (%) 
-111< 0.1%
 
-9.92< 0.1%
 
-91< 0.1%
 
-7.12< 0.1%
 
-43< 0.1%
 
-2.92< 0.1%
 
-25< 0.1%
 
-0.97< 0.1%
 
-0.41< 0.1%
 
0.11< 0.1%
 
ValueCountFrequency (%) 
9999.91690.1%
 
93.21< 0.1%
 
90.91< 0.1%
 
89.11< 0.1%
 
87.11< 0.1%
 
84.94< 0.1%
 
83.81< 0.1%
 
82.92< 0.1%
 
82.41< 0.1%
 
823< 0.1%
 

flag_min
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing82202
Missing (%)50.5%
Memory size1.2 MiB
*
70423 
10303 
ValueCountFrequency (%) 
*7042343.2%
 
103036.3%
 
(Missing)8220250.5%
 
2021-05-10T00:15:58.440404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:58.501820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:58.575061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length1.945822695
Min length0

Overview of Unicode Properties

Unique unicode characters3
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n16440451.9%
 
a8220225.9%
 
*7042322.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter24660677.8%
 
Other Punctuation7042322.2%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
*70423100.0%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n16440466.7%
 
a8220233.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin24660677.8%
 
Common7042322.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
*70423100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n16440466.7%
 
a8220233.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII317029100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n16440451.9%
 
a8220225.9%
 
*7042322.2%
 

prcp
Real number (ℝ≥0)

ZEROS

Distinct305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.61121072
Minimum0
Maximum99.99
Zeros96446
Zeros (%)59.2%
Memory size1.2 MiB
2021-05-10T00:15:58.689885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.15
95-th percentile99.99
Maximum99.99
Range99.99
Interquartile range (IQR)0.15

Descriptive statistics

Standard deviation31.92483784
Coefficient of variation (CV)2.749483978
Kurtosis3.79416013
Mean11.61121072
Median Absolute Deviation (MAD)0
Skewness2.407011622
Sum1891791.34
Variance1019.195271
MonotocityNot monotonic
2021-05-10T00:15:58.812202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
09644659.2%
 
99.991880511.5%
 
0.0157023.5%
 
0.0233772.1%
 
0.0425811.6%
 
0.0320601.3%
 
0.0818721.1%
 
0.0515020.9%
 
0.1214010.9%
 
0.0613960.9%
 
0.0712230.8%
 
0.1611700.7%
 
0.0910360.6%
 
0.19910.6%
 
0.29380.6%
 
0.119000.6%
 
0.138210.5%
 
0.248030.5%
 
0.147760.5%
 
0.157380.5%
 
0.286790.4%
 
0.176680.4%
 
0.186380.4%
 
0.195900.4%
 
0.355250.3%
 
Other values (280)152909.4%
 
ValueCountFrequency (%) 
09644659.2%
 
0.0157023.5%
 
0.0233772.1%
 
0.0320601.3%
 
0.0425811.6%
 
0.0515020.9%
 
0.0613960.9%
 
0.0712230.8%
 
0.0818721.1%
 
0.0910360.6%
 
ValueCountFrequency (%) 
99.991880511.5%
 
5.511< 0.1%
 
5.122< 0.1%
 
5.081< 0.1%
 
5.041< 0.1%
 
51< 0.1%
 
4.991< 0.1%
 
4.981< 0.1%
 
4.881< 0.1%
 
4.721< 0.1%
 

flag_prcp
Categorical

MISSING

Distinct8
Distinct (%)< 0.1%
Missing18803
Missing (%)11.5%
Memory size1.2 MiB
G
84362 
I
37195 
D
10040 
H
 
4089
A
 
3013
Other values (3)
 
5426
ValueCountFrequency (%) 
G8436251.8%
 
I3719522.8%
 
D100406.2%
 
H40892.5%
 
A30131.8%
 
B28101.7%
 
C26141.6%
 
2< 0.1%
 
(Missing)1880311.5%
 
2021-05-10T00:15:58.929797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:58.997871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:59.112699image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.230801336
Min length0

Overview of Unicode Properties

Unique unicode characters9
Unique unicode categories2 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
G8436242.1%
 
n3760618.8%
 
I3719518.5%
 
a188039.4%
 
D100405.0%
 
H40892.0%
 
A30131.5%
 
B28101.4%
 
C26141.3%
 

Most occurring categories

ValueCountFrequency (%) 
Uppercase Letter14412371.9%
 
Lowercase Letter5640928.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
G8436258.5%
 
I3719525.8%
 
D100407.0%
 
H40892.8%
 
A30132.1%
 
B28101.9%
 
C26141.8%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n3760666.7%
 
a1880333.3%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin200532100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
G8436242.1%
 
n3760618.8%
 
I3719518.5%
 
a188039.4%
 
D100405.0%
 
H40892.0%
 
A30131.5%
 
B28101.4%
 
C26141.3%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII200532100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
G8436242.1%
 
n3760618.8%
 
I3719518.5%
 
a188039.4%
 
D100405.0%
 
H40892.0%
 
A30131.5%
 
B28101.4%
 
C26141.3%
 

sndp
Real number (ℝ≥0)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean996.7036759
Minimum0.4
Maximum999.9
Zeros0
Zeros (%)0.0%
Memory size1.2 MiB
2021-05-10T00:15:59.217258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile999.9
Q1999.9
median999.9
Q3999.9
95-th percentile999.9
Maximum999.9
Range999.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.3793909
Coefficient of variation (CV)0.05656585028
Kurtosis307.1490328
Mean996.7036759
Median Absolute Deviation (MAD)0
Skewness-17.58243108
Sum162390936.5
Variance3178.635718
MonotocityNot monotonic
2021-05-10T00:15:59.328318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%) 
999.916240699.7%
 
0.41280.1%
 
21000.1%
 
1.2960.1%
 
3.142< 0.1%
 
0.839< 0.1%
 
1.618< 0.1%
 
3.917< 0.1%
 
2.416< 0.1%
 
2.812< 0.1%
 
5.111< 0.1%
 
7.18< 0.1%
 
9.17< 0.1%
 
4.74< 0.1%
 
5.94< 0.1%
 
7.94< 0.1%
 
3.53< 0.1%
 
9.83< 0.1%
 
8.31< 0.1%
 
6.31< 0.1%
 
32.71< 0.1%
 
16.51< 0.1%
 
12.21< 0.1%
 
37.41< 0.1%
 
17.71< 0.1%
 
Other values (3)3< 0.1%
 
ValueCountFrequency (%) 
0.41280.1%
 
0.839< 0.1%
 
1.2960.1%
 
1.618< 0.1%
 
21000.1%
 
2.416< 0.1%
 
2.812< 0.1%
 
3.142< 0.1%
 
3.53< 0.1%
 
3.917< 0.1%
 
ValueCountFrequency (%) 
999.916240699.7%
 
37.41< 0.1%
 
36.21< 0.1%
 
32.71< 0.1%
 
17.71< 0.1%
 
16.51< 0.1%
 
15.71< 0.1%
 
12.21< 0.1%
 
9.83< 0.1%
 
9.17< 0.1%
 

fog
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
125851 
1
37077 
ValueCountFrequency (%) 
012585177.2%
 
13707722.8%
 
2021-05-10T00:15:59.438692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:59.498367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:59.567614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
012585177.2%
 
13707722.8%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number162928100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
012585177.2%
 
13707722.8%
 

Most occurring scripts

ValueCountFrequency (%) 
Common162928100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
012585177.2%
 
13707722.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162928100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
012585177.2%
 
13707722.8%
 

rain_drizzle
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
96936 
1
65992 
ValueCountFrequency (%) 
09693659.5%
 
16599240.5%
 
2021-05-10T00:15:59.667789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:59.731919image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:59.801582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
09693659.5%
 
16599240.5%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number162928100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
09693659.5%
 
16599240.5%
 

Most occurring scripts

ValueCountFrequency (%) 
Common162928100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
09693659.5%
 
16599240.5%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162928100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
09693659.5%
 
16599240.5%
 

snow_ice_pellets
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
158990 
1
 
3938
ValueCountFrequency (%) 
015899097.6%
 
139382.4%
 
2021-05-10T00:15:59.903278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:15:59.969379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:16:00.041694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
015899097.6%
 
139382.4%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number162928100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
015899097.6%
 
139382.4%
 

Most occurring scripts

ValueCountFrequency (%) 
Common162928100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
015899097.6%
 
139382.4%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162928100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
015899097.6%
 
139382.4%
 

hail
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
162735 
1
 
193
ValueCountFrequency (%) 
016273599.9%
 
11930.1%
 
2021-05-10T00:16:00.145641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:16:00.218436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:16:00.289736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
016273599.9%
 
11930.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number162928100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
016273599.9%
 
11930.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common162928100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
016273599.9%
 
11930.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162928100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
016273599.9%
 
11930.1%
 

thunder
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
161073 
1
 
1853
1000
 
2
ValueCountFrequency (%) 
016107398.9%
 
118531.1%
 
10002< 0.1%
 
2021-05-10T00:16:00.400573image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2021-05-10T00:16:00.477727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:16:00.558520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length1
Mean length1.000036826
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
016107998.9%
 
118551.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number162934100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
016107998.9%
 
118551.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common162934100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
016107998.9%
 
118551.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII162934100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
016107998.9%
 
118551.1%
 
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
161116 
10000
 
1703
11000
 
62
1
 
25
10010
 
19
Other values (2)
 
3
ValueCountFrequency (%) 
016111698.9%
 
1000017031.0%
 
1100062< 0.1%
 
125< 0.1%
 
1001019< 0.1%
 
10002< 0.1%
 
101001< 0.1%
 
2021-05-10T00:16:00.671116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2021-05-10T00:16:00.747046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:16:00.858601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length1
Mean length1.043859864
Min length1

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories1 ?
Unique unicode scripts1 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
016818098.9%
 
118941.1%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number170074100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
016818098.9%
 
118941.1%
 

Most occurring scripts

ValueCountFrequency (%) 
Common170074100.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
016818098.9%
 
118941.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII170074100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
016818098.9%
 
118941.1%
 

Interactions

2021-05-10T00:15:16.791222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:16.911246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.017817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.137800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.248443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.360234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.571878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.684787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.792436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:17.895505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.008190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.115343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.234788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.347455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.465050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.579247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.702779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.831355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:18.957879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.094004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.224204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.350737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.473272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.599266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.727184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.855059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:19.975285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.091893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.208783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.335513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.460170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.572496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.689437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.833801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:20.972090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.111035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.252112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.508288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.641147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.764257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:21.897549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.024209image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.147426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.271022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.391959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.513282image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.634450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.756718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.877399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:22.990123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.103436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.241074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.354572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.462581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.567631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.676858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.784399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:23.891990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.011788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.134725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.248836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.357565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.466327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.572474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.678547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.788037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:24.902894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.028734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.146498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.259073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.373110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.479770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.593929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.709661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:25.824140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.069118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.192816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.306363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.422258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.534965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.653009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.771898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:26.888405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.007255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.120385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.231774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.341613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.452984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.565488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.676696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.786971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:27.904637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.014135image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.127508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.238414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.349077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.459690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.577265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.694496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.816853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:28.930974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.048188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.171268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.288288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.402793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.516716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.641466image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.769606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:29.896103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.014685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.131129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.257210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.386487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.510413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.630108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.764445image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:30.890093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.014983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.138487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.264632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.391596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.518774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.794958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:31.920847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.033486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.150754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.271434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.399015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.523440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.646451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.772268image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:32.908043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.034633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.158334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.279291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.400875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.521582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.647475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.773972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:33.898287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.022262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.145639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.268748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.390271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.511173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.631380image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.751317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.878320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:34.996392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.124118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.246988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.374393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.497917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.620947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.744150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.861647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:35.979293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.099913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.221188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.340307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.461002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.582254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.704975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.833394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:36.958778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.082163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.203328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.323183image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.441109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.559930image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.684418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.807092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:37.932883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.057410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.180407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.301759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.424371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.543096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.662122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:38.791554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.113208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.240836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.361307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.481432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.603120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.724114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.844563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:39.967104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.089198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.210795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.330672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.451449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.571993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.695613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.817305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:40.948543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.075549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.197853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.315687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.435914image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.558038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.677440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.798998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:41.922497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.041921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.164940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.286582image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.409778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.530521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.652334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.775253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:42.909881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.031847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.152683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.271073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.391806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.512942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.633042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.753068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.874777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:43.996861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.119260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.244353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.366585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.488652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.611409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.733941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.861977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:44.985324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.107201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.224858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.340981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.458234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.570493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.682916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.798239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:45.926361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.046990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.168576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.287629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.409179image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.530837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.650702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.781095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:46.902956image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.025235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.144989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.263936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.382539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.505255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.626809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:47.981353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:48.108852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:48.230683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:48.351828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:48.472886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-10T00:16:00.977710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-10T00:16:01.176760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-10T00:16:01.373638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-10T00:16:01.601921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-10T00:16:01.865832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-10T00:15:49.287463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:50.664770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:51.569467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-10T00:15:51.952633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexstnwbandateyearmodatempcount_tempdewpcount_dewpslpcount_slpstpcount_stpvisibcount_visibwdspcount_wdspmxpsdgustmaxflag_maxminflag_minprcpflag_prcpsndpfograin_drizzlesnow_ice_pelletshailthundertornado_funnel_cloud
0072694599999None1943051752.02343.8239999.909999.9022.3237.12313.0999.966.4*38.3*0.00I999.9000000
1172694599999None1943070460.12347.3231022.8239999.9019.9237.02313.0999.973.4*49.3*0.00I999.9000000
2272694599999None1943072372.92357.6231019.4239999.9014.9235.82313.0999.989.2*57.4*0.00I999.9000000
3372694599999None1943122835.62332.7221017.5239999.908.5233.5239.9999.942.4*28.4*0.00I999.9000000
4472698024229None1943092565.52354.6231008.8239999.905.6234.42314.0999.995.4*52.3*0.00I999.9000000
5572698024229None1943012322.62413.724999.1249999.907.3246.92414.0999.930.4*15.4*99.99None999.9001000
6672698024229None1943012422.92416.2241016.6249999.903.6245.72412.0999.930.4*11.3*99.99None999.9001000
7772694599999None1943042151.31240.7129999.909999.9013.81213.61218.1999.957.4*45.3*99.99None999.9010000
8872694599999None1943061959.42349.0231014.4239999.9023.3237.82313.0999.968.4*52.3*99.99None999.9010000
9972694599999None1943062560.92350.9231020.5239999.9012.5236.52311.1999.970.3*54.3*99.99None999.9010000

Last rows

df_indexstnwbandateyearmodatempcount_tempdewpcount_dewpslpcount_slpstpcount_stpvisibcount_visibwdspcount_wdspmxpsdgustmaxflag_maxminflag_minprcpflag_prcpsndpfograin_drizzlesnow_ice_pelletshailthundertornado_funnel_cloud
16291830612726980242292020-01-142020011437.72433.2241013.2209.0249.9244.22412999.941.0None35.1None0.18G999.91100011000
16291930613726881942732020-01-152020011531.92429.6241012.8226.7249.1243.6249.9999.941.0None27.0None0.00G999.91100011000
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